Table REF  shows the performance of different methods on the historical document competition H-DIBCO 2014 data set.
Fig. REF  shows the enhanced and corresponding binarization maps computed by the proposed DeepOtsu (SR) method on this data set.
From Table REF  we can see that the performance of our proposed DeepOtsu methods is comparable to the Vo's method [1]} which also uses deep learning.
The performance of the traditional methods, such as Howe [2]}, Su [3]} and Jia [4]}, is the comparable to the deep learning methods, such as Vo [1]}, which indicates that the binarization problem on the H-DIBCO 2014 data set is less challenging than other data sets.
The best performance is achieved by the Vo's method [1]}, which integrates outputs of the binary predictions of three different networks and the final binary map is computed by a local and global threshold that is learned on a separate data set.
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